code: def observe(self, r_t, s_t, s_t1, a_t, done):
      if self.is_training:
          self.memory.append(s_t, a_t, r_t, done)
  
imports: import numpy
  import torch
  import torch.nn
  from torch.optim import Adam
  from .memory import SequentialMemory
  from .utils import to_numpy, to_tensor
label_desc: Use image node_id 5 for calling the DDPG obj's underlying member method code with example usage: obj.observe(r_t, s_t, s_t1, a_t, done) without return types